Sorry, I feel that I should know this, but can Mplus handle two-level models and the use of replicate weights at the same time?

For example, suppose the sampling design consisted of stratified selection of PSUs, then within PSUs, schools were selected and then students within schools. The replicate weights could accommodate the first level of sampling while the model addresses the 2nd and 3rd.

I know that the sandwich estimator for the SEs is available if you have stratum and PSU info, but what if we only have replicate weights on the file?

How can we acknowledge the clustering of cases within higher-level units, while using replicate weights?

In my data, I have students nested in teachers/classrooms, and I have a set of replicate weights for the teachers, such that results generalize to all teachers in the U.S.

The replicate weights account for the unequal probability of a teacher to be sampled, but I need to employ CLUSTER = teachID to account for the nesting of students on level 1 within teachers on level 2.

The CLUSTER command is typically used with TYPE = TWOLEVEL.

Is there any way to account for the nested structure of the data, while still using the replicate weights?

Alternatively, I have both level-1 and level-2 weights. I just don't have stratum and PSU identifiers for all cases.

If I use TYPE=TWOLEVEL in conjunction with the WEIGHT and BWEIGHT commands to weight units on the two levels, will this adequately account for the stratification done prior to sampling, such that I don't need to specify COMPLEX on the TYPE line as well?

That is, will using WEIGHT and BWEIGHT with TYPE=TWOLEVEL make it such that I do not need to specify stratum? Does using the weights for each level account for stratification?